Month: February 2016

One thing that online video distributors (such as Netflix) don’t do yet (and that they will most certainly do in the future–some of the tech is already in place) is to take into account the content of the movies, such that they can learn a user’s likes in a way that is independent from (and complimentary to) the likes of other users with similar preferences.

It’s been possible for while, for example, to automatically identify “types” of documents and to tag or cluster those documents with a high degree of accuracy. Early last year, using a euclidean-distance algorithm that worked on trigram-vector representations of text, I created an example of just such a thing, document affinity, using Wikipedia articles. In the example, you could choose a Wikipedia article and the system would list the other Wikipedia articles in order of similarity to the one you chose. Despite the fact that this method is rather well known, the results of that experiment were nothing short of astonishing, and provided a quality of links among Wikipedia articles that exceeds anything that exists today, much better even than the human-made links that are in the articles. Moreover, consider that the bag-of-trigrams method I just described is archaic by comparison to some of the algorithms that research groups are exploring today, like recurrent neural networks using word vectors (my favorite).

Imagine if, in addition to resorting to the standard preference algorithms, an online video distributor (such as Eros Now or Netflix) were to analyze the script of the movie (subtitles, for example), identify similar movie scripts, and account for that when choosing other movies that you might like. The distributor’s predictions would improve in a dramatic way.

The technology to analyze the text of the movies exists already. But, if you look into the future, you’ll find that soon computers will be able to analyze the visual part of the movie, the frames themselves, the motion, and the sounds. Computers will be able to combine that type of analysis with script analysis for a full-content analysis that will increase the preference predictions even more markedly. Google and others have already started to do some work in related areas, largely based on the contributions to deep learning that Geoffrey Hinton and his students have made in the last decade, including rectified linear units, dropout, ways of combining convolutional neural-network layers with fully-connected layers for higher abstraction, and word-vectors and phrase-vector training.

We’re going to see far better preference predictions in the near future, probably by smaller companies in the beginning. In the long run, this kind of prediction, and the more advanced ones that I described above, which encroach into the realm of human cognitive abilities, will become widespread.